James H Collier1, Lloyd Allison1, Arthur M Lesk2, Peter J Stuckey3, Maria Garcia de la Banda1, Arun S Konagurthu1. 1. Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia. 2. Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA. 3. Department of Computing and Information Systems, University of Melbourne, Parkville, VIC 3010, Australia.
Abstract
Motivation: Structural molecular biology depends crucially on computational techniques that compare protein three-dimensional structures and generate structural alignments (the assignment of one-to-one correspondences between subsets of amino acids based on atomic coordinates). Despite its importance, the structural alignment problem has not been formulated, much less solved, in a consistent and reliable way. To overcome these difficulties, we present here a statistical framework for the precise inference of structural alignments, built on the Bayesian and information-theoretic principle of Minimum Message Length (MML). The quality of any alignment is measured by its explanatory power-the amount of lossless compression achieved to explain the protein coordinates using that alignment. Results: We have implemented this approach in MMLigner , the first program able to infer statistically significant structural alignments. We also demonstrate the reliability of MMLigner 's alignment results when compared with the state of the art. Importantly, MMLigner can also discover different structural alignments of comparable quality, a challenging problem for oligomers and protein complexes. Availability and Implementation: Source code, binaries and an interactive web version are available at http://lcb.infotech.monash.edu.au/mmligner . Contact: arun.konagurthu@monash.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Structural molecular biology depends crucially on computational techniques that compare protein three-dimensional structures and generate structural alignments (the assignment of one-to-one correspondences between subsets of amino acids based on atomic coordinates). Despite its importance, the structural alignment problem has not been formulated, much less solved, in a consistent and reliable way. To overcome these difficulties, we present here a statistical framework for the precise inference of structural alignments, built on the Bayesian and information-theoretic principle of Minimum Message Length (MML). The quality of any alignment is measured by its explanatory power-the amount of lossless compression achieved to explain the protein coordinates using that alignment. Results: We have implemented this approach in MMLigner , the first program able to infer statistically significant structural alignments. We also demonstrate the reliability of MMLigner 's alignment results when compared with the state of the art. Importantly, MMLigner can also discover different structural alignments of comparable quality, a challenging problem for oligomers and protein complexes. Availability and Implementation: Source code, binaries and an interactive web version are available at http://lcb.infotech.monash.edu.au/mmligner . Contact: arun.konagurthu@monash.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Sandun Rajapaksa; Dinithi Sumanaweera; Arthur M Lesk; Lloyd Allison; Peter J Stuckey; Maria Garcia de la Banda; David Abramson; Arun S Konagurthu Journal: Bioinformatics Date: 2022-06-24 Impact factor: 6.931